A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM

• Published in: IEEE transactions on automation science and engineering Vol. 19; no. 3; pp. 1 - 11
• Main Authors: Bi, Jing, Zhang, Xiang, Yuan, Haitao, Zhang, Jia, Zhou, MengChu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Cloud computing; Communications traffic; Data centers; Datasets; Deep learning; Edge computing; Feature extraction; Industrial areas; Load modeling; Long short-term memory (LSTM); machine learning; network traffic prediction; Neural networks; Noise prediction; Predictive models; Real time; Savitzky-Golay (SG) filter; Sequences; Support vector machines; Task analysis; temporal convolutional network (TCN); Time series; Time series analysis
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2021.3077537

Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the performance of a network and analyze its health status. In recent years, neural networks have been proved suita...